mlsanityData to 7 Oct 2026

Developing AI Agents with Simulated Data: Why, what, and how?

arXiv 2602.15816 ↗First posted 17 Feb 2026cs.AI cs.ETcited by 0

Not on the map. Why?

Abstract (arXiv)

As insufficient data volume and quality remain the key impediments to the adoption of modern subsymbolic AI, techniques of synthetic data generation are in high demand. Simulation offers an apt, systematic approach to generating diverse synthetic data. This chapter introduces the reader to the key concepts, benefits, and challenges of simulation-based synthetic data generation for AI training purposes, and to a reference framework to describe, design, and analyze digital twin-based AI simulation solutions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 35 of its 116 references that are in the map. First posted on arXiv 17 Feb 2026.

Builds on

35 of 116 references.

References · 35

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2016Improved Techniques for Training GANscited by 2,938
  3. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  4. 2018Learning Dexterous In-Hand Manipulationcited by 643
  5. 2024Evaluating Real-World Robot Manipulation Policies in Simulationcited by 484
  6. 2017Robust Adversarial Reinforcement Learningcited by 439
  7. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  8. 2016Learning to reinforcement learncited by 369
  9. 2016Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networkscited by 307
  10. 2018Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomizationcited by 292
  11. 2018Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experiencecited by 203
  12. 2019Digital Twin: Values, Challenges and Enablerscited by 81
23 more
  1. 2019Robust Reinforcement Learning for Continuous Control with Model Misspecificationcited by 76
  2. 2021Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulationcited by 59
  3. 2021Imitation Learning: Progress, Taxonomies and Challengescited by 56
  4. 2021Robot Learning from Randomized Simulations: A Reviewcited by 56
  5. 2023How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligencecited by 55
  6. 2023Synthetic Datasets for Autonomous Driving: A Surveycited by 48
  7. 2025A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Modelscited by 36
  8. 2017Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulationcited by 35
  9. 2024CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Trafficcited by 22
  10. 2023LightSim: Neural Lighting Simulation for Urban Scenescited by 20
  11. 2021A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directionscited by 19
  12. 2020Learning Precise 3D Manipulation from Multiple Uncalibrated Camerascited by 18
  13. 2023S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Realitycited by 18
  14. 2019Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twincited by 17
  15. 2024WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamicscited by 14
  16. 2024Bridging the Synthetic-to-Authentic Gap: Distortion-Guided Unsupervised Domain Adaptation for Blind Image Quality Assessmentcited by 9
  17. 2025AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecturecited by 6
  18. 2023Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systemscited by 5
  19. 2024Digital Twin Evolution for Sustainable Smart Ecosystemscited by 4
  20. 2023Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systemscited by 3
  21. 2024Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approachcited by 3
  22. 2026Introduction to Digital Twins for the Smart Gridcited by 3
  23. 2025Engineering Automotive Digital Twins on Standardized Architectures: A Case Studycited by 2

Built on by

Not cited yet.